# SPDX-License-Identifier: Apache-2.0

import math

import torch
import torch.nn as nn

from fastvideo.layers.activation import get_act_fn
from fastvideo.layers.linear import ReplicatedLinear
from fastvideo.layers.mlp import MLP


class PatchEmbed(nn.Module):
    """2D Image to Patch Embedding

    Image to Patch Embedding using Conv2d

    A convolution based approach to patchifying a 2D image w/ embedding projection.

    Based on the impl in https://github.com/google-research/vision_transformer

    Hacked together by / Copyright 2020 Ross Wightman

    Remove the _assert function in forward function to be compatible with multi-resolution images.
    """

    def __init__(self,
                 patch_size=16,
                 in_chans=3,
                 embed_dim=768,
                 norm_layer=None,
                 flatten=True,
                 bias=True,
                 dtype=None,
                 prefix: str = ""):
        super().__init__()
        # Convert patch_size to 2-tuple
        if isinstance(patch_size, list | tuple):
            if len(patch_size) == 1:
                patch_size = (patch_size[0], patch_size[0])
        else:
            patch_size = (patch_size, patch_size)

        self.patch_size = patch_size
        self.flatten = flatten

        self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x


class WanCamControlPatchEmbedding(nn.Module):
    """Lingbot World Patch embedding for Plucker features."""

    def __init__(
            self,
            patch_size=(1, 2, 2),
            in_chans=384,  # 6 * 64
            embed_dim=2048,
            bias=True,
            dtype=None,
            prefix: str = ""):
        super().__init__()
        # must be 3-tuple
        if isinstance(patch_size, list | tuple):
            if len(patch_size) != 3:
                raise ValueError(f"patch_size must have length 3, got {len(patch_size)}")
        else:
            raise ValueError(f"Unsupported patch_size type: {type(patch_size)}")

        self.patch_size = patch_size
        pt, ph, pw = self.patch_size
        self.in_features = in_chans * pt * ph * pw
        self.proj = nn.Linear(self.in_features, embed_dim, bias=bias, dtype=dtype)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if x.dim() != 5:
            raise ValueError(f"Expected camera embedding shape [B, C, F, H, W], got {x.shape}")
        bsz, channels, frames, height, width = x.shape
        pt, ph, pw = self.patch_size
        if (frames % pt) != 0 or (height % ph) != 0 or (width % pw) != 0:
            raise ValueError(f"Input shape {x.shape} must be divisible by patch_size {self.patch_size}")

        # '1 c (f c1) (h c2) (w c3) -> 1 (f h w) (c c1 c2 c3)',
        x = x.view(
            bsz,
            channels,
            frames // pt,
            pt,
            height // ph,
            ph,
            width // pw,
            pw,
        )
        x = x.permute(0, 2, 4, 6, 1, 3, 5, 7).reshape(bsz, -1, self.in_features)
        return self.proj(x)


class TimestepEmbedder(nn.Module):
    """
    Embeds scalar timesteps into vector representations.
    """

    def __init__(
        self,
        hidden_size,
        act_layer="silu",
        frequency_embedding_size=256,
        max_period=10000,
        dtype=None,
        freq_dtype=torch.float32,
        prefix: str = "",
    ):
        super().__init__()
        self.frequency_embedding_size = frequency_embedding_size
        self.max_period = max_period

        self.mlp = MLP(frequency_embedding_size, hidden_size, hidden_size, act_type=act_layer, dtype=dtype)
        self.freq_dtype = freq_dtype

    def forward(self, t: torch.Tensor, timestep_seq_len: int | None = None) -> torch.Tensor:
        t_freq = timestep_embedding(t, self.frequency_embedding_size, self.max_period,
                                    dtype=self.freq_dtype).to(self.mlp.fc_in.weight.dtype)
        if timestep_seq_len is not None:
            t_freq = t_freq.unflatten(0, (1, timestep_seq_len))
        # t_freq = t_freq.to(self.mlp.fc_in.weight.dtype)
        t_emb = self.mlp(t_freq)
        return t_emb


def timestep_embedding(t: torch.Tensor,
                       dim: int,
                       max_period: int = 10000,
                       dtype: torch.dtype = torch.float32) -> torch.Tensor:
    """
    Create sinusoidal timestep embeddings.
    
    Args:
        t: Tensor of shape [B] with timesteps
        dim: Embedding dimension
        max_period: Controls the minimum frequency of the embeddings
        
    Returns:
        Tensor of shape [B, dim] with embeddings
    """
    half = dim // 2
    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=dtype) / half).to(device=t.device)
    args = t[:, None].float() * freqs[None]
    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
    if dim % 2:
        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    return embedding


class ModulateProjection(nn.Module):
    """Modulation layer for DiT blocks."""

    def __init__(
        self,
        hidden_size: int,
        factor: int = 2,
        act_layer: str = "silu",
        dtype: torch.dtype | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.factor = factor
        self.hidden_size = hidden_size
        self.linear = ReplicatedLinear(hidden_size, hidden_size * factor, bias=True, params_dtype=dtype)
        self.act = get_act_fn(act_layer)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.act(x)
        x, _ = self.linear(x)
        return x


def unpatchify(x, t, h, w, patch_size, channels) -> torch.Tensor:
    """
    Convert patched representation back to image space.
    
    Args:
        x: Tensor of shape [B, T*H*W, C*P_t*P_h*P_w]
        t, h, w: Temporal and spatial dimensions
        
    Returns:
        Unpatchified tensor of shape [B, C, T*P_t, H*P_h, W*P_w]
    """
    assert x.ndim == 3, f"x.ndim: {x.ndim}"
    assert len(patch_size) == 3, f"patch_size: {patch_size}"
    assert t * h * w == x.shape[1], f"t * h * w: {t * h * w}, x.shape[1]: {x.shape[1]}"
    c = channels
    pt, ph, pw = patch_size

    x = x.reshape(shape=(x.shape[0], t, h, w, c, pt, ph, pw))
    x = torch.einsum("nthwcopq->nctohpwq", x)
    imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))

    return imgs


def get_timestep_embedding(
    timesteps: torch.Tensor,
    embedding_dim: int,
    flip_sin_to_cos: bool = False,
    downscale_freq_shift: float = 1,
    scale: float = 1,
    max_period: int = 10000,
) -> torch.Tensor:
    """
    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
    Args
        timesteps (torch.Tensor):
            a 1-D Tensor of N indices, one per batch element. These may be fractional.
        embedding_dim (int):
            the dimension of the output.
        flip_sin_to_cos (bool):
            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
        downscale_freq_shift (float):
            Controls the delta between frequencies between dimensions
        scale (float):
            Scaling factor applied to the embeddings.
        max_period (int):
            Controls the maximum frequency of the embeddings
    Returns
        torch.Tensor: an [N x dim] Tensor of positional embeddings.
    """
    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"

    half_dim = embedding_dim // 2
    exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device)
    exponent = exponent / (half_dim - downscale_freq_shift)

    emb = torch.exp(exponent)
    emb = timesteps[:, None].float() * emb[None, :]

    # scale embeddings
    emb = scale * emb

    # concat sine and cosine embeddings
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)

    # flip sine and cosine embeddings
    if flip_sin_to_cos:
        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)

    # zero pad
    if embedding_dim % 2 == 1:
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb


class Timesteps(nn.Module):

    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
        super().__init__()
        self.num_channels = num_channels
        self.flip_sin_to_cos = flip_sin_to_cos
        self.downscale_freq_shift = downscale_freq_shift
        self.scale = scale

    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
        t_emb = get_timestep_embedding(
            timesteps,
            self.num_channels,
            flip_sin_to_cos=self.flip_sin_to_cos,
            downscale_freq_shift=self.downscale_freq_shift,
            scale=self.scale,
        )
        return t_emb